This paper presents a hand gesture based control of an omnidirectional wheelchair using inertial measurement unit (IMU) and myoelectric units as wearable sensors. Seven common gestures are recognized and classified using shape based feature extraction and Dendogram Support Vector Machine (DSVM) classifier. The dynamic gestures are mapped to the omnidirectional motion commands to navigate the wheelchair. A single IMU is used to measure the wrist tilt angle and acceleration in three axis. EMG signals are extracted from two forearm muscles namely Extensor Carpi Radialis and Flexor Carpi Radialis and processed to provide Root Mean Square (RMS) signal. Initiation and termination of dynamic activities are based on autonomous identification of static to dynamic or dynamic to static transition by setting static thresholds on processed IMU and myoelectric sensor data. Classification involves recognizing the activity pattern based on periodic shape of trajectories of the triaxial wrist tilt angle and EMG-RMS from the two selected muscles. Second order Polynomial coefficients extracted from the sensor trajectory templates during specific dynamic activity cycles are used as features to classify dynamic activities. Classification algorithm and real time navigation of the wheelchair using the proposed algorithm has been tested by five healthy subjects. Classification accuracy of 94% was achieved by DSVM classifier on ‘k’ fold cross validation data of 5 users. Classification accuracy while operating the wheelchair was 90.5%.

Hand Gesture Recognition Based Omnidirectional Wheelchair Control Using IMU and EMG Sensors

Design and Performance Evaluation of 4 Wheeled Omni Wheelchair with Reduced Slip and Vibration

Omnidirectional Assistive Wheelchair

A lot of people have neuromuscular problems that affect their lives leading them to lose an important degree of autonomy in their daily activities. When their disabilities do not involve speech disorders, robotic wheelchairs with voice assistant technologies may provide appropriate human–robot interaction for them. Given the wide improvement and diffusion of Google Assistant, Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa, etc., such voice assistant technologies can be fully integrated and exploited in robotic wheelchairs to improve the quality of life of affected people. As such, in this paper, we propose an abstraction layer capable of providing appropriate human–robot interaction. It allows use of voice assistant tools that may trigger different kinds of applications for the interaction between the robot and the user. Furthermore, we propose a use case as a possible instance of the considered abstraction layer. Within the use case, we chose existing tools for each component of the proposed abstraction layer. For example, Google Assistant was employed as a voice assistant tool; its functions and APIs were leveraged for some of the applications we deployed. On top of the use case thus defined, we created several applications that we detail and discuss. The benefit of the resulting Human–Computer Interaction is therefore two-fold: on the one hand, the user may interact with any of the developed applications; on the other hand, the user can also rely on voice assistant tools to receive answers in the open domain when the statement of the user does not enable any of the applications of the robot. An evaluation of the presented instance was carried out using the Software Architecture Analysis Method, whereas the user experience was evaluated through ad-hoc questionnaires. Our proposed abstraction layer is general and can be instantiated on any robotic platform including robotic wheelchairs.

An Abstraction Layer Exploiting Voice Assistant Technologies for Effective Human—Robot Interaction

This research study here deals with the Stochastic Petri-Nets (SPNs) modeling of an interacting task (getting up from a sitting position) in a human-machine interaction model. The machine is an Intelligent Robotic Wheelchair (IRW) appropriately modified to provide assistance to a human user, who utilizes the IRW. SPNs are recognized as useful modeling tools for analyzing the reliability and performance of systems through time, which is the main reason of choosing them. The main use of this model is to simulate the procedure and eventually used by people with disabilities and the elderly with ultimate goal to improve their daily life. They will get up from a sitting position with the help of the wheelchair, which has robotic arms attached to it. If not for the IRW, these people would need the assistance of another person to get up. Simulated results will be provided from the stage of this study.

An SPN Modeling of the H-IRW Getting-Up Task

In today’s world there are many disabled persons who find it difficult to perform movements or perform daily activities. This types of persons are mainly dependent on others for their assistance. But they can become self-independent and perform some daily activities on their own with the help of assistive devices. The most widely used assistive devices are Wheelchairs. Wheelchairs is basically a chair fitted with wheels, which can help people move around who cannot walk because of illness, disability or injury. But there are many disabled people with weak limbs and joints who cannot move the wheelchair. Thus, Smart Wheelchair can benefit a lot to them and everyone in society. Smart Wheelchairs are electric powered wheelchairs with many extra components such as a computer and sensors which help the user or guardian accompanying wheelchair to handle it easily and efficiently. The recent development in the field of Artificial Intelligence, Sensor technologies and Robotics help the growth of wheelchairs with new features. This paper is to review the current state of art of Smart Wheelchairs and discuss the future research in this field.

http://ijict.iaescore.com/index.php/IJICT/article/download/11812/9156

Darshitkumar Suratwala

Papers

Smart Wheelchair: A Literature Review